In telemedicine systems, critical medical data is shared on a public communication channel. This increases the risk of unauthorised access to patient’s information. This underlines the importance of secrecy and authentication for the medical data. This paper presents two innovative variations of classical histogram shift methods to increase the hiding capacity. The first technique divides the image into nonoverlapping blocks and embeds the watermark individually using the histogram method. The second method separates the region of interest and embeds the watermark only in the region of noninterest. This approach preserves the medical information intact. This method finds its use in critical medical cases. The high PSNR (above 45 dB) obtained for both techniques indicates imperceptibility of the approaches. Experimental results illustrate superiority of the proposed approaches when compared with other methods based on histogram shifting techniques. These techniques improve embedding capacity by 5–15% depending on the image type, without affecting the quality of the watermarked image. Both techniques also enable lossless reconstruction of the watermark and the host medical image. A higher embedding capacity makes the proposed approaches attractive for medical image watermarking applications without compromising the quality of the image.
Rapid growth in the internet and multimedia technology in the recent times poses threat to the authentication and secured transmission of multimedia data. In telemedicine applications, the medical images are exchanged to facilitate improved patient’s clinical status [
Various algorithms have been proposed by various researchers for reversible watermarking. These techniques can be categorised in to five classes, namely, (i) integer transform based, (ii) data compression based, (iii) based on histogram bin shifting, (iv) prediction of pixel values based, and (v) based on modification of frequency domain characteristics [
The histogram bin shifting-based methods are popular because of the ease of implementation and the overhead generated is lesser. It makes use of the histogram of the original image, modifies selected portion of the histogram near the peak point, and then embeds the secret data into the image. The scheme was initially proposed by Ni et al. [
The next section gives the details about the implemented methods. Section
In this section, the novel approaches for reversible watermarking are discussed. Firstly, the classical histogram shift method is revisited. Then, the block-wise embedding technique for the watermarking is explained. While embedding watermark in the medical image, care needs to be taken in order that the region of interest in the image is not tempered. To achieve this, the third method is proposed where the region of noninterest is separated from the image and the watermark is embedded only in the region of noninterest. The performance evaluation parameters used for determining the imperceptibility and the quality are the peak signal-to-noise ratio (PSNR), the mean square error (MSE) [
Mean square error estimates the average of the square of the errors. The lower the value of MSE, the lower the errors and the higher the imperceptibility.
Higher PSNR indicates higher imperceptibility of the watermark in the image.
In the last three decades, a great deal of effort has gone into the development of quality assessment methods that take advantage of known characteristics of the human visual system (HVS). Here, a measure of structural similarity (SSIM) that compares local patterns of pixel intensities that have been normalized for luminance and contrast is used to access the similarity between the original and the watermarked image. The structural similarity (SSIM) index [
Generally, SSIM is calculated for blocks of the image and then mean SSIM (MSSIM) is used as the performance parameter.
In the general histogram shift method proposed by Ni et al. [
Consider
To demonstrate the various techniques, different modality medical images are considered here. The image sizes ranged from 544 × 304 to 1002 × 1132. Figure
Original medical image.
Watermark image.
The results were obtained with the help of MATLAB. The image in Figure
Watermarked image.
To improve the limited watermarking capacity provided by the classical histogram shift technique, here, a new method is proposed. In this technique, the first cover medical image is divided into nonoverlapping blocks of equal size. The blocks are chosen to be nonoverlapping so a pixel will not be embedded with multiple bits. The histogram is calculated for each block and the maximum count greyscale value is noted along with the maximum count. Then, the watermark is embedded in each block. Each block can accommodate watermark bits equal to the maximum count in that block. For the image shown in Figure
Further, the algorithm is tested for varied block sizes like 4 × 4, 8 × 8, and 16 × 16. It is observed that as the block size increases, the embedding capacity decreases as excepted. In this technique, though the histogram needs to be calculated for each block, the overall complexity of the algorithm is still simpler compared to that in other reversible watermarking algorithms. Figure
Watermarked image for block size 4 × 4.
While hiding the watermark in a medical image, it is very important that the region of interest in the image should be kept unaltered. This is very important for the medical conclusions [
Image with the region of interest (ROI).
This ROI is separated using Out’s method [
Watermark embedded in the RONI.
All the reversible watermarking techniques explained in the previous section are tried on various types of medical images. The algorithms are implemented using MATLAB.
The first implemented method performs the watermark embedding based on the classical histogram shift technique. Table
Results of the classical histogram shift technique.
Images | Image size | Hiding capacity | MSE | PSNR |
---|---|---|---|---|
Cover1 | 900 × 854 | 44,417 | 0.9831 | 47.92 |
Cover2 | 1002 × 1132 | 36,947 | 1.086 | 47.49 |
Cover3 | 544 × 304 | 37,651 | 1.076 | 47.53 |
For a given image, the watermark image is resized to the maximum hiding capacity for that image and the watermark is embedded using the histogram shift method. It can be concluded that with this technique, the PSNR value achieved is very high. So, the method is imperceptible. The calculations are very simple—only those required for plotting histogram of the image. So, the method is easy to implement.
The classical histogram shift technique has limited hiding capacity which changes from image to image. To improve the embedding capacity, a novel variant of the classical histogram shift technique is proposed in Section
Comparison of the hiding capacity for novel variants with the classical histogram shift technique.
Image | Image size | Hiding capacity in bits (histogram shift) | Block-wise histogram shift in bits (4 × 4) | Block-wise histogram shift in bits (8 × 8) | Block-wise histogram shift in bits (16 × 16) | Embedding in non-ROI in bits |
---|---|---|---|---|---|---|
Cover1 | 900 × 854 | 44,417 | 48,164 | 46,201 | 45,367 | 44,494 |
Cover2 | 1002 × 1132 | 36,947 | 42,258 | 39,567 | 38,253 | 37,103 |
Cover3 | 544 × 304 | 37,651 | 43,784 | 41,211 | 39,499 | 37,862 |
MSE, PSNR, and MSSIM values for the block-wise embedding technique.
Images | Block size 4 × 4 | Block size 8 × 8 | Block size 16 × 16 | ||||||
---|---|---|---|---|---|---|---|---|---|
MSE | PSNR | SSIM | MSE | PSNR | SSIM | MSE | PSNR | SSIM | |
Cover1 | 0.8266 | 48.68 | 0.9149 | 0.8481 | 48.56 | 0.9146 | 0.8831 | 48.39 | 0.9148 |
Cover2 | 0.7822 | 49.02 | 0.9273 | 0.7962 | 48.96 | 0.9297 | 0.8187 | 48.82 | 0.9271 |
Cover3 | 0.7668 | 49.28 | 0.9325 | 0.7766 | 49.23 | 0.9321 | 0.8022 | 49.08 | 0.9317 |
In the new technique discussed in Section
MSE, PSNR, and MSSIM values for the technique using non-ROI.
Image name | MSE | PSNR | MSSIM |
---|---|---|---|
Cover1 | 0.6013 | 49.99 | 0.9386 |
Cover2 | 0.3431 | 52.63 | 0.9659 |
Cover3 | 0.5292 | 50.72 | 0.9476 |
Most of the watermarking techniques modify the host image and thereby distort it while embedding the watermark. In many applications, these distortions or loss of fidelity is acceptable if the original image and the watermarked image are perceptually equivalent. On the contrary, images in some typical applications have stringent constraints on the image fidelity and thus, distortions during watermarking are not acceptable. Some of the application areas wherein images have stringent constraints on the image fidelity are medical images, military surveillance images, spy-satellite images, and legal document images. The reversible watermarking techniques, as discussed in this paper, are recommended for such applications mainly due to the lossless property of watermarking. It may be noted that the techniques presented in this paper give special emphasis on the reversibility property. In principle, these techniques can be applied to all types of images; however, its effectiveness gets highlighted in the applications where reversibility of the image is imperative.
Both the novel techniques proposed are reversible, so they can extract the watermark as well as the original medical image accurately.
Medical data exchange on the communication channel needs to be secured and authenticated. This can be achieved by means of watermarking the patient’s data in the medical image itself. These watermarking techniques have to be carefully chosen as the data embedded should not hinder the vital medical information. Also, the techniques chosen should be reversible, that is, the cover medical image as well as the watermark should be extracted accurately. In this research work, the classical histogram technique is discussed with its application for medical images. To overcome the limited available watermarking capacity of this algorithm, two innovative techniques are suggested. For the block-wise embedding technique, it is observed that with a smaller block size like 4 × 4, higher embedding capacity is accomplished compared to larger block sizes like 8 × 8 or 16 × 16. The quantitative results indicate high imperceptibly with this technique. The second technique embeds watermark only in the region of noninterest. This technique provides lesser improvement in the hiding capacity compared to the block-wise technique. But in case of critical medical images where small distortion in the region of interest cannot be tolerated, this technique changes only the region of noninterest. Moreover, both the techniques preserve the simplicity of the original method. These innovative variants are well-suited techniques for medical image watermarking with higher capacity and better imperceptibility. These techniques are more suitable and beneficial for the applications that put stringent restriction on the image distortion.
The authors declare that there is no conflict of interest regarding the publication of this paper to the best of their knowledge.